CN114329337A - Method and system for predicting insulation resistance of electric vehicle and storage medium - Google Patents
Method and system for predicting insulation resistance of electric vehicle and storage medium Download PDFInfo
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Abstract
The invention discloses an electric vehicle insulation resistance prediction method, a system and a storage medium, wherein the method comprises the steps of obtaining multiple groups of resistance associated data, including vehicle operation data, battery pack operation data and a current insulation resistance value of a battery pack, wherein the vehicle operation data comprises rapid acceleration times, rapid turning times, vehicle bumping times and air conditioner operation time, and the battery pack operation data comprises high-current charge-discharge time, high-temperature charge-discharge time, charge overcurrent times and high-humidity charge-discharge time; constructing an insulation resistance prediction model of the vehicle based on a multivariate nonlinear regression technology; training an insulation resistance prediction model by using resistance correlation data; and predicting the insulation resistance according to the trained insulation resistance prediction model. According to the method, the system and the storage medium for predicting the insulation resistance of the electric vehicle, provided by the embodiment of the invention, the insulation resistance prediction model is constructed by adopting a nonlinear regression technology based on the resistance correlation data to identify the insulation failure risk in advance, so that the prediction precision of the insulation resistance is improved.
Description
Technical Field
The invention relates to the technical field of power batteries, in particular to a method and a system for predicting insulation resistance of an electric vehicle and a storage medium.
Background
At present, although all the electric devices on the high-voltage system loop of the electric automobile are equipped with insulation protection measures, the phenomenon of insulation failure still exists. When insulation failure occurs, the whole automobile body is connected with a high-voltage system and carries current, the voltage of the high-voltage system of the electric automobile is up to more than 400V, and serious electric shock injury can be caused if a human body contacts the automobile body.
Regarding the estimation of the insulation resistance, the prior art is developed around single insulation resistance data, and the influence of other related data is not considered, so that the prediction accuracy of the insulation resistance is not high, and the limitation is large.
Disclosure of Invention
The invention provides a method, a system and a storage medium for predicting insulation resistance of an electric vehicle, which are used for solving the technical problem of low prediction precision of the existing insulation resistance.
In order to solve the above technical problem, an embodiment of the present invention provides a method for predicting an insulation resistance of an electric vehicle, including:
acquiring multiple groups of resistance associated data, wherein each group of resistance associated data comprises vehicle operation data, battery pack operation data and current insulation resistance of a battery pack, the vehicle operation data comprises rapid acceleration times, rapid turning times, vehicle bumping times and air conditioner operation time, and the battery pack operation data comprises high-current charge-discharge time, high-temperature charge-discharge time, charge overcurrent times and high-humidity charge-discharge time;
constructing an insulation resistance prediction model of the vehicle based on a multivariate nonlinear regression technology;
training the insulation resistance prediction model by using the resistance correlation data;
and predicting the insulation resistance of the vehicle battery pack according to the trained insulation resistance prediction model.
As one preferable scheme, the vehicle operation data, the battery pack operation data and the current insulation resistance value of the battery pack are acquired through a vehicle-mounted T-Box.
As one of the preferable schemes, the vehicle-mounted T-Box obtains the high-humidity charge-discharge time, the air-conditioner operation time and the current insulation resistance value through a vehicle body area controller;
the vehicle-mounted T-Box obtains the times of sharp acceleration, the times of sharp turning and the times of vehicle bump through a chassis domain controller;
and the vehicle-mounted T-Box acquires the high-current charging and discharging time length, the high-temperature charging and discharging time length and the charging overcurrent times through a power domain controller.
As one preferable scheme, the constructing of the insulation resistance prediction model of the vehicle based on the multiple nonlinear regression technology specifically includes:
the method comprises the following steps of taking the current insulation resistance value of a battery pack as an output item of a multiple nonlinear regression equation, taking vehicle operation data and battery pack operation data as input items of the multiple nonlinear regression equation, and constructing the multiple nonlinear regression equation as follows:
y＝β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8
wherein y is the current insulation resistance value of the battery pack, and x1For the number of rapid accelerations, beta1Is the coefficient of rapid acceleration, x2For number of sharp turns, beta2Is the coefficient of sharp turn, x3Number of jounces of vehicle, beta3As coefficient of pitch, x4For the duration of operation of the air conditioner, beta4Is the air conditioning coefficient, x5For long periods of high current charging and discharging, beta5For high current charge-discharge coefficient, x6For long time of high temperature charge and discharge, beta6Is a high temperature charge-discharge coefficient, x7For number of charging overcurrent, beta7For charging over-current coefficient, x8For long time of high humidity charge and discharge, beta8The high humidity charge-discharge coefficient.
As one preferable scheme, the rapid acceleration coefficient, the rapid turning coefficient, the bumping coefficient, the air conditioning coefficient, the high current charge-discharge time length, the high temperature charge-discharge coefficient, the charge overcurrent coefficient, and the high humidity charge-discharge coefficient are obtained by identifying through a least square method.
As one preferable scheme, after acquiring a plurality of sets of the resistance-related data, the method for predicting the insulation resistance of the electric vehicle further includes:
and carrying out data cleaning on the plurality of groups of resistance related data.
As one of preferable solutions, the insulation resistance prediction model of the vehicle is constructed at a data edge layer of the vehicle.
As one preferred scheme, the training of the insulation resistance prediction model by using the resistance correlation data specifically includes:
uploading the insulation resistance prediction model to a cloud big data platform;
updating the resistance correlation data based on a cloud database;
and training the insulation resistance prediction model by using the updated resistance correlation data.
The invention provides a system for predicting the insulation resistance of an electric vehicle, which comprises a cloud big data platform, a vehicle-mounted T-Box, and a vehicle body domain controller, a chassis domain controller and a power domain controller which are respectively controlled by the vehicle-mounted T-Box;
the cloud big data platform is in communication connection with the vehicle-mounted T-Box;
the electric vehicle insulation resistance prediction system is used for realizing the electric vehicle insulation resistance prediction method.
Still another embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, wherein when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the method for predicting the insulation resistance of an electric vehicle as described above.
Compared with the prior art, the embodiment of the invention has the advantages that at least one point is as follows: the method is characterized in that the method is not limited to a single insulation resistor, but other associated data except the insulation resistor are surrounded, the number of times of rapid acceleration, the number of times of rapid turning, the number of times of vehicle bumping and the length of air conditioner operation representing the vehicle operation are respectively obtained, the length of high-current charging and discharging, the length of high-temperature charging and discharging, the number of charging and overcurrent and the length of high-humidity charging and discharging representing the battery pack operation are combined, the construction and training of an insulation resistor prediction model are carried out through a multivariate nonlinear regression equation by combining the current insulation resistance value of the battery pack, the accuracy of model prediction is improved, the vehicle can be monitored for insulation resistor failure, the risk of insulation failure is recognized in advance, and the safety accidents of vehicle leakage, passenger electric shock and the like caused by the insulation failure are avoided.
Drawings
FIG. 1 is a schematic flow chart of an insulation resistance prediction method for an electric vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of model training in one embodiment of the present invention;
FIG. 3 is a schematic illustration of prediction in one embodiment of the present invention;
FIG. 4 is a block diagram of an electric vehicle insulation resistance prediction system in one embodiment of the present invention;
reference numerals:
1. a cloud big data platform; 2. vehicle-mounted T-Box; 3. a body area controller; 4. a chassis domain controller; 5. a power domain controller.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of the present application, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In the description of the present application, it is to be noted that, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention, as those skilled in the art will recognize the specific meaning of the terms used in the present application in a particular context.
An embodiment of the present invention provides a method for predicting insulation resistance of an electric vehicle, and specifically, referring to fig. 1, fig. 1 is a schematic flow chart of the method for predicting insulation resistance of an electric vehicle in an embodiment of the present invention, including steps S1 to S4:
s1, obtaining multiple groups of resistance associated data, wherein each group of resistance associated data comprises vehicle operation data, battery pack operation data and current insulation resistance of a battery pack, the vehicle operation data comprises rapid acceleration times, rapid turning times, vehicle bumping times and air conditioner operation time, and the battery pack operation data comprises high-current charge-discharge time, high-temperature charge-discharge time, charge overcurrent times and high-humidity charge-discharge time;
s2, constructing an insulation resistance prediction model of the vehicle based on a multiple nonlinear regression technology;
s3, training the insulation resistance prediction model by using the resistance correlation data;
and S4, predicting the insulation resistance of the vehicle battery pack according to the trained insulation resistance prediction model.
For an electric automobile, insulation failure comprises two conditions, one is instantaneous failure, namely contact between a high-voltage system and an automobile body or damage of an insulation protection device in a very short time, so that the insulation resistance value is rapidly reduced to be below a dangerous threshold value, and the instantaneous failure generally occurs when the automobile collides; and the other is long-term failure, namely the insulation resistance value tends to decline along with the frequent use of the vehicle and the aging of the line until the vehicle completely loses the insulation protection function. According to the invention, the change condition of the insulation resistance value of the vehicle is analyzed based on the associated data aiming at the scene of long-term insulation failure, and finally, the effect of predicting the insulation failure in advance can be realized.
In the embodiment of the invention, the insulation resistance is not limited to a single insulation resistance, but other associated data except the surrounding insulation resistance, and for the associated data, the associated data can bring certain influence on the insulation resistance of the battery pack of the electric vehicle, for example, the running time of an air conditioner in the embodiment can change the ambient temperature in the vehicle, and the insulation resistance of the battery pack can certainly change to a certain extent at different ambient temperatures in the vehicle; for example, the number of times of vehicle bumping can bring about a certain degree of mechanical vibration, and different vibration frequencies can also cause the battery pack to collide, thereby bringing about a certain degree of influence on the insulation resistance value. In the embodiment, data related to the insulation resistance is divided into two categories, one category is vehicle operation data considered from the vehicle driving perspective, and the other category is battery pack operation data considered from the battery pack, and then the current insulation resistance value of the battery pack is combined to construct a prototype of the prediction model. Of course, in this embodiment, the obtained multiple sets of resistance related data are preferably related data obtained by selecting N vehicles within 3 months, so as to widen the range of the sample set used as basic data and provide good data support for the training of subsequent models.
Further, in the above-described embodiment, the vehicle operation data, the battery pack operation data, and the current insulation resistance value of the battery pack are acquired by an on-vehicle T-Box. It should be emphasized that the obtaining manner of the resistance related data in this embodiment needs to be adjusted for different vehicle architectures, for example, all signals may be collected by a single domain controller (e.g., a vehicle domain controller), or signals may be collected by a vehicle body domain controller, a chassis domain controller, and a power domain controller and shared on the CAN. The data are acquired from the vehicle body domain, the chassis domain and the power domain, so that resource waste caused by mutual redundancy of different ECU computing power can be saved, and the data acquisition process is quicker and more centralized.
Specifically, in this embodiment, the high-humidity charge-discharge time, the air-conditioner operation time, and the current insulation resistance value are obtained by a vehicle body area controller; acquiring the times of sharp acceleration, the times of sharp turning and the times of vehicle bump through a chassis domain controller; and acquiring the high-current charging and discharging time, the high-temperature charging and discharging time and the charging overcurrent times through a power domain controller. Preferably, no matter what manner is adopted for data acquisition, the data is finally uploaded through the vehicle-mounted T-box, that is, all data should be sent to the vehicle-mounted T-box regardless of the source, and details are not repeated herein.
The nonlinear regression prediction method is a method for establishing a nonlinear model according to nonlinear relations between a plurality of independent variables and dependent variables and then fitting measured data to obtain model parameters. In this embodiment, each associated data represents a feature item that affects the insulation resistance, including an environmental factor and a human factor, and then, based on a multiple nonlinear regression technique, each feature item is used as an input of a regression equation, a current insulation resistance value is used as an output of the regression equation, and model parameters are identified and fitted according to a plurality of sets of data input, that is, the insulation resistance prediction model is trained through input of a plurality of sets of resistance associated data, and the identified model parameters are normalized to obtain final model parameters.
Specifically, a multivariate nonlinear regression equation is constructed as follows:
y＝β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8
wherein y is the current insulation resistance value of the battery pack, and x1For the number of rapid accelerations, beta1Is the coefficient of rapid acceleration, x2For number of sharp turns, beta2Is the coefficient of sharp turn, x3Number of jounces of vehicle, beta3As coefficient of pitch, x4For the duration of operation of the air conditioner, beta4Is the air conditioning coefficient, x5For long periods of high current charging and discharging, beta5For high current charge-discharge coefficient, x6For long time of high temperature charge and discharge, beta6Is a high temperature charge-discharge coefficient, x7For number of charging overcurrent, beta7For charging over-current coefficient, x8For long time of high humidity charge and discharge, beta8The high humidity charge-discharge coefficient.
Beta is as defined above1、β2……β8The coefficients representing the model parameters to be identified are preferably identified by the least squares method in this embodiment, which is a method proposed in the prior art to solve the unknown parameters, as follows:
the equation for one mathematical model is:
y0＝β0+β1x1+β2x2+……+βkxk
in order to calculate the model parameters, we need to test the system experimentally to obtain the measured values of y and the feature items x1, x2 … … xk
And then establishing a least square equation.
We will first note its matrix form as:
the least squares function is defined as:
applying partial differential equation to obtain:
let the above equation equal 0, solve to get:
β＝(XTX)-1XTY
further, in the above embodiment, in order to find and correct recognizable errors in the data, including checking data consistency, processing invalid values and missing values, and the like, data cleaning needs to be performed on a plurality of sets of the resistance-related data after the data is acquired, of course, there are a plurality of methods for data cleaning, and in the embodiment, the data is cleaned by using a 3 σ criterion, so that the prediction accuracy of the final insulation resistance value is improved.
In the presence of a large amount of vehicle information data, the data processing mode can determine the data processing efficiency, in order not to affect the normal operation of the vehicle, in this embodiment, the insulation resistance prediction model of the vehicle is constructed on the data edge layer of the vehicle, that is, on the software level, the model is deployed on the data edge section, the data is preprocessed and cached on the edge layer, and the insulation resistance value of the vehicle is predicted and monitored under the condition that the normal use of the vehicle is not affected, so that the failure time and possible failure reasons can be identified before the insulation failure of the battery pack occurs, and a driver can conveniently take counter measures to the vehicle in advance.
Specifically, referring to fig. 2, fig. 2 is a schematic flow chart illustrating model training in an embodiment of the present invention, in the above embodiment, for step S3: training the insulation resistance prediction model by using the resistance correlation data, wherein the training specifically comprises the following steps:
s31, uploading the insulation resistance prediction model to a cloud big data platform;
s32, updating the resistance correlation data based on a cloud database;
and S33, training the insulation resistance prediction model by using the updated resistance correlation data.
The model parameters are updated by real-time training of the cloud, the prediction accuracy of the insulation resistance prediction model can be further improved, relevant charts can be drawn on a cloud monitoring platform, and predicted insulation resistance data can be managed and analyzed. Referring to fig. 3, fig. 3 is a schematic diagram illustrating a prediction obtained by the method for predicting insulation resistance of an electric vehicle according to the embodiment of the invention, wherein a dotted line portion in the diagram is a predicted insulation resistance value.
In another embodiment of the present invention, referring to fig. 4, fig. 4 is a block diagram of an electric vehicle insulation resistance prediction system according to an embodiment of the present invention, which includes a cloud big data platform 1, a vehicle-mounted T-Box2, and a vehicle body domain controller 3, a chassis domain controller 4, and a power domain controller 5 controlled by the vehicle-mounted T-Box2 respectively;
the cloud big data platform 1 is in communication connection with the vehicle-mounted T-Box 2;
the electric vehicle insulation resistance prediction system is used for realizing the electric vehicle insulation resistance prediction method.
Accordingly, an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where the computer program, when running, controls a device in which the computer-readable storage medium is located to perform the steps in the method for predicting insulation resistance of an electric vehicle according to the above embodiment, such as steps S1 to S4 shown in fig. 1.
The method, the system and the storage medium for predicting the insulation resistance of the electric automobile have the advantages that at least one point is as follows:
the method is characterized in that the method is not limited to a single insulation resistor, but other associated data except the insulation resistor are surrounded, the number of times of rapid acceleration, the number of times of rapid turning, the number of times of vehicle bumping and the length of air conditioner operation representing the vehicle operation are respectively obtained, the length of high-current charging and discharging, the length of high-temperature charging and discharging, the number of charging and overcurrent and the length of high-humidity charging and discharging representing the battery pack operation are combined, the construction and training of an insulation resistor prediction model are carried out through a multivariate nonlinear regression equation by combining the current insulation resistance value of the battery pack, the accuracy of model prediction is improved, the vehicle can be monitored for insulation resistor failure, the risk of insulation failure is recognized in advance, and the safety accidents of vehicle leakage, passenger electric shock and the like caused by the insulation failure are avoided.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. An electric vehicle insulation resistance prediction method is characterized by comprising the following steps:
acquiring multiple groups of resistance associated data, wherein each group of resistance associated data comprises vehicle operation data, battery pack operation data and current insulation resistance of a battery pack, the vehicle operation data comprises rapid acceleration times, rapid turning times, vehicle bumping times and air conditioner operation time, and the battery pack operation data comprises high-current charge-discharge time, high-temperature charge-discharge time, charge overcurrent times and high-humidity charge-discharge time;
constructing an insulation resistance prediction model of the vehicle based on a multivariate nonlinear regression technology;
training the insulation resistance prediction model by using the resistance correlation data;
and predicting the insulation resistance of the vehicle battery pack according to the trained insulation resistance prediction model.
2. The method for predicting the insulation resistance of the electric vehicle according to claim 1, wherein the vehicle operation data, the battery pack operation data and the current insulation resistance value of the battery pack are acquired through an on-vehicle T-Box.
3. The insulation resistance prediction method for the electric vehicle according to claim 2, wherein the vehicle-mounted T-Box obtains the high humidity charging and discharging time period, the air conditioner operation time period and the current insulation resistance value through a vehicle body domain controller;
the vehicle-mounted T-Box obtains the times of sharp acceleration, the times of sharp turning and the times of vehicle bump through a chassis domain controller;
and the vehicle-mounted T-Box acquires the high-current charging and discharging time length, the high-temperature charging and discharging time length and the charging overcurrent times through a power domain controller.
4. The method for predicting the insulation resistance of the electric vehicle according to claim 1, wherein the constructing of the insulation resistance prediction model of the vehicle based on the multivariate nonlinear regression technique specifically comprises:
the method comprises the following steps of taking the current insulation resistance value of a battery pack as an output item of a multiple nonlinear regression equation, taking vehicle operation data and battery pack operation data as input items of the multiple nonlinear regression equation, and constructing the multiple nonlinear regression equation as follows:
y＝β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8
wherein y is the current insulation resistance value of the battery pack, and x1For the number of rapid accelerations, beta1Is the coefficient of rapid acceleration, x2For number of sharp turns, beta2Is the coefficient of sharp turn, x3Number of jounces of vehicle, beta3As coefficient of pitch, x4For the duration of operation of the air conditioner, beta4Is the air conditioning coefficient, x5For long periods of high current charging and discharging, beta5For high current charge-discharge coefficient, x6For long time of high temperature charge and discharge, beta6Is a high temperature charge-discharge coefficient, x7To chargeNumber of times of current flow, beta7For charging over-current coefficient, x8For long time of high humidity charge and discharge, beta8The high humidity charge-discharge coefficient.
5. The method for predicting the insulation resistance of the electric vehicle according to claim 4, wherein the rapid acceleration coefficient, the rapid turning coefficient, the bumping coefficient, the air conditioning coefficient, the high current charge-discharge time, the high temperature charge-discharge coefficient, the charge overcurrent coefficient, and the high humidity charge-discharge coefficient are obtained by identifying through a least square method.
6. The method of predicting insulation resistance of an electric vehicle according to claim 1, wherein after obtaining the plurality of sets of the resistance-related data, the method of predicting insulation resistance of an electric vehicle further comprises:
and carrying out data cleaning on the plurality of groups of resistance related data.
7. The method of claim 1, wherein the insulation resistance prediction model of the vehicle is constructed at a data edge layer of the vehicle.
8. The method for predicting the insulation resistance of the electric vehicle according to claim 1, wherein the training of the insulation resistance prediction model by using the resistance correlation data specifically comprises:
uploading the insulation resistance prediction model to a cloud big data platform;
updating the resistance correlation data based on a cloud database;
and training the insulation resistance prediction model by using the updated resistance correlation data.
9. The system for predicting the insulation resistance of the electric automobile is characterized by comprising a cloud big data platform, a vehicle-mounted T-Box, and a vehicle body domain controller, a chassis domain controller and a power domain controller which are respectively controlled by the vehicle-mounted T-Box;
the cloud big data platform is in communication connection with the vehicle-mounted T-Box;
the electric vehicle insulation resistance prediction system is used for realizing the electric vehicle insulation resistance prediction method according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program runs, the computer-readable storage medium is controlled to execute the method for predicting insulation resistance of an electric vehicle according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116027110A (en) * | 2023-02-20 | 2023-04-28 | 中汽数据有限公司 | Online early warning method, medium and equipment for new energy automobile |
| CN116559535A (en) * | 2023-02-15 | 2023-08-08 | 苏州共元自控技术有限公司 | Insulation monitoring equipment for direct-current charging pile |
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2021
- 2021-11-26 CN CN202111427866.7A patent/CN114329337A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116559535A (en) * | 2023-02-15 | 2023-08-08 | 苏州共元自控技术有限公司 | Insulation monitoring equipment for direct-current charging pile |
| CN116559535B (en) * | 2023-02-15 | 2023-11-10 | 苏州共元自控技术有限公司 | Insulation monitoring equipment for direct-current charging pile |
| CN116027110A (en) * | 2023-02-20 | 2023-04-28 | 中汽数据有限公司 | Online early warning method, medium and equipment for new energy automobile |
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